Modeling morphologically rich languages using splitwords and unstructured dependencies

Abstract

We experiment with splitting words into their stem and suffix components for modeling morphologically rich languages. We show that using a morphological analyzer and disambiguator results in a significant perplexity reduction in Turkish. We present flexible n-gram models, Flex-Grams, which assume that the n-1 tokens that determine the probability of a given token can be chosen anywhere in the sentence rather than the preceding n-1 positions. Our final model achieves 27% perplexity reduction compared to the standard n-gram model.